Why agreement with humans does not prove LLM alignment

Alignment in artificial intelligence refers to the process of ensuring that a model's outputs match human intent, values, and ethical standards. A common proxy for this alignment is measuring how often a large language model (LLM) agrees with human-provided labels on moral dilemmas. However, recent research indicates that agreement with human judgments is not a reliable indicator of true moral alignment. Two agents may reach the same conclusion while relying on entirely different ethical frameworks, logical pathways, or situational interpretations.

Does reaching the same conclusion mean a model is aligned?

Reaching the same conclusion as a human does not prove that an LLM is morally aligned with human values. While a model may select the same final label as a majority of human annotators, it often arrives at that decision using a different set of moral priorities or logical justifications. Research published in 2026 indicates that while frontier and open-source models often achieve high agreement rates with human labels, they systematically diverge when prompted to provide the reasoning behind those choices (arXiv:2608.12368).

How do LLMs and humans differ in moral reasoning?

Models and humans prioritize different moral categories when evaluating a situation, even when they reach the same final verdict. Human moral judgment is typically informed by a complex interplay of empathy, social norms, and nuanced contextual understanding. In contrast, LLMs redistribute their "attention" across specific moral categories—such as harm, promise-keeping, justice, and desert—in ways that do not mirror human cognitive processes. Data shows that even when a model matches a human's final choice, its internal logic often over-indexes on certain categories while neglecting others that a human would consider central to the decision (arXiv:2608.12368).

Why is label-based evaluation misleading?

Label-based evaluation is a method of testing AI performance by comparing a model’s output directly against a pre-existing dataset of human-approved answers. Relying solely on this method is misleadingly reassuring because it obscures the underlying reasoning process. If a developer only checks if the model says "yes" or "no" to a moral dilemma, they remain blind to the faulty or misaligned logic that might have led to that answer. Without analyzing the supporting rationales, it is impossible to determine if a model is truly safe or if it is merely "gaming" the benchmark by mimicking the statistical distribution of human labels.